Conventionally, when large documents are created, a user must manually extract information and create new individual documents based on the extracted information. For example, a university may create a large ADOBE ACROBAT .PDF file to represent user information for each student (e.g. thousands of students). Similarly, large corporations, businesses, hospitals, etc. may log user information of clients, employees, etc., and place the information in one or more large documents with many pages. The file may have a large file size among other attributes which are difficult to manage.
Continuing with the school examples, certain examples of reports could include: report cards, progress reports, truancy/attendance letters, bus assignment information, locker assignments, course schedules, personalized permission/registration forms. For example, conventional applications would generate report cards, resulting in a 600 page PDF-type file, with report cards for 600 students. Each page would generally have the student's ID number among other forms of student information. When uploading this PDF file, the system would recognize the student ID on each page and extract the page(s) appropriate for each student. A user would then be able to view the resulting report for any given student as a QA check, prior to initiating the broadcast, and see any unique identifiers that couldn't be matched. This approach would of course only work on native report PDFs with recognizable characters as opposed to purely image/scanned documents character recognition. This feature would also be applicable in an AUTOMESSENGER product. It's common for businesses to use this type of functionality to transmit invoices to customers for example, using a customer ID on each page.